Overview

Dataset statistics

Number of variables15
Number of observations420551
Missing cells0
Missing cells (%)0.0%
Duplicate rows327
Duplicate rows (%)0.1%
Total size in memory48.1 MiB
Average record size in memory120.0 B

Variable types

Categorical1
Numeric14

Alerts

Dataset has 327 (0.1%) duplicate rowsDuplicates
Date Time has a high cardinality: 420224 distinct valuesHigh cardinality
T (degC) is highly overall correlated with Tpot (K) and 8 other fieldsHigh correlation
Tpot (K) is highly overall correlated with T (degC) and 8 other fieldsHigh correlation
Tdew (degC) is highly overall correlated with T (degC) and 6 other fieldsHigh correlation
rh (%) is highly overall correlated with T (degC) and 3 other fieldsHigh correlation
VPmax (mbar) is highly overall correlated with T (degC) and 8 other fieldsHigh correlation
VPact (mbar) is highly overall correlated with T (degC) and 6 other fieldsHigh correlation
VPdef (mbar) is highly overall correlated with T (degC) and 4 other fieldsHigh correlation
sh (g/kg) is highly overall correlated with T (degC) and 6 other fieldsHigh correlation
H2OC (mmol/mol) is highly overall correlated with T (degC) and 6 other fieldsHigh correlation
rho (g/m**3) is highly overall correlated with T (degC) and 7 other fieldsHigh correlation
wv (m/s) is highly overall correlated with max. wv (m/s)High correlation
max. wv (m/s) is highly overall correlated with wv (m/s)High correlation
wv (m/s) is highly skewed (γ1 = -152.7160913)Skewed
max. wv (m/s) is highly skewed (γ1 = -144.7488651)Skewed
Date Time is uniformly distributedUniform

Reproduction

Analysis started2023-09-12 13:12:56.336156
Analysis finished2023-09-12 13:13:22.459161
Duration26.12 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date Time
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct420224
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
21.03.2014 12:50:00
 
2
01.07.2010 03:50:00
 
2
01.07.2010 04:10:00
 
2
01.07.2010 04:20:00
 
2
01.07.2010 04:30:00
 
2
Other values (420219)
420541 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters7990469
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419897 ?
Unique (%)99.8%

Sample

1st row01.01.2009 00:10:00
2nd row01.01.2009 00:20:00
3rd row01.01.2009 00:30:00
4th row01.01.2009 00:40:00
5th row01.01.2009 00:50:00

Common Values

ValueCountFrequency (%)
21.03.2014 12:50:00 2
 
< 0.1%
01.07.2010 03:50:00 2
 
< 0.1%
01.07.2010 04:10:00 2
 
< 0.1%
01.07.2010 04:20:00 2
 
< 0.1%
01.07.2010 04:30:00 2
 
< 0.1%
01.07.2010 04:40:00 2
 
< 0.1%
01.07.2010 04:50:00 2
 
< 0.1%
01.07.2010 05:00:00 2
 
< 0.1%
01.07.2010 05:10:00 2
 
< 0.1%
01.07.2010 05:20:00 2
 
< 0.1%
Other values (420214) 420531
> 99.9%

Length

2023-09-12T20:13:22.498474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14:50:00 2922
 
0.3%
15:30:00 2922
 
0.3%
14:00:00 2922
 
0.3%
15:00:00 2922
 
0.3%
15:10:00 2922
 
0.3%
15:20:00 2922
 
0.3%
13:00:00 2922
 
0.3%
13:10:00 2922
 
0.3%
13:20:00 2922
 
0.3%
13:30:00 2922
 
0.3%
Other values (3055) 811882
96.5%

Most occurring characters

ValueCountFrequency (%)
0 2600557
32.5%
1 1082312
13.5%
2 912743
 
11.4%
. 841102
 
10.5%
: 841102
 
10.5%
420551
 
5.3%
3 273325
 
3.4%
5 234752
 
2.9%
4 233781
 
2.9%
6 163198
 
2.0%
Other values (3) 387046
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5887714
73.7%
Other Punctuation 1682204
 
21.1%
Space Separator 420551
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2600557
44.2%
1 1082312
18.4%
2 912743
 
15.5%
3 273325
 
4.6%
5 234752
 
4.0%
4 233781
 
4.0%
6 163198
 
2.8%
9 162674
 
2.8%
7 112228
 
1.9%
8 112144
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 841102
50.0%
: 841102
50.0%
Space Separator
ValueCountFrequency (%)
420551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7990469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2600557
32.5%
1 1082312
13.5%
2 912743
 
11.4%
. 841102
 
10.5%
: 841102
 
10.5%
420551
 
5.3%
3 273325
 
3.4%
5 234752
 
2.9%
4 233781
 
2.9%
6 163198
 
2.0%
Other values (3) 387046
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7990469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2600557
32.5%
1 1082312
13.5%
2 912743
 
11.4%
. 841102
 
10.5%
: 841102
 
10.5%
420551
 
5.3%
3 273325
 
3.4%
5 234752
 
2.9%
4 233781
 
2.9%
6 163198
 
2.0%
Other values (3) 387046
 
4.8%

p (mbar)
Real number (ℝ)

Distinct6117
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean989.21278
Minimum913.6
Maximum1015.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:22.545785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum913.6
5-th percentile974.95
Q1984.2
median989.58
Q3994.72
95-th percentile1002.49
Maximum1015.35
Range101.75
Interquartile range (IQR)10.52

Descriptive statistics

Standard deviation8.3584807
Coefficient of variation (CV)0.0084496287
Kurtosis0.93352594
Mean989.21278
Median Absolute Deviation (MAD)5.25
Skewness-0.40702279
Sum4.1601442 × 108
Variance69.8642
MonotonicityNot monotonic
2023-09-12T20:13:22.600131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990.96 469
 
0.1%
989.73 453
 
0.1%
991.08 447
 
0.1%
989.36 427
 
0.1%
991.2 415
 
0.1%
990.71 413
 
0.1%
989.48 409
 
0.1%
988.87 405
 
0.1%
990.59 401
 
0.1%
989.24 399
 
0.1%
Other values (6107) 416313
99.0%
ValueCountFrequency (%)
913.6 1
< 0.1%
914.1 1
< 0.1%
917.4 2
< 0.1%
918.3 1
< 0.1%
918.5 1
< 0.1%
942.43 1
< 0.1%
942.54 1
< 0.1%
942.58 1
< 0.1%
942.59 1
< 0.1%
942.62 1
< 0.1%
ValueCountFrequency (%)
1015.35 1
 
< 0.1%
1015.3 1
 
< 0.1%
1015.29 2
< 0.1%
1015.28 1
 
< 0.1%
1015.26 1
 
< 0.1%
1015.23 1
 
< 0.1%
1015.21 1
 
< 0.1%
1015.2 1
 
< 0.1%
1015.19 1
 
< 0.1%
1015.17 3
< 0.1%

T (degC)
Real number (ℝ)

Distinct5530
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4501474
Minimum-23.01
Maximum37.28
Zeros109
Zeros (%)< 0.1%
Negative55837
Negative (%)13.3%
Memory size3.2 MiB
2023-09-12T20:13:22.727589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-23.01
5-th percentile-3.86
Q13.36
median9.42
Q315.47
95-th percentile23.15
Maximum37.28
Range60.29
Interquartile range (IQR)12.11

Descriptive statistics

Standard deviation8.4233652
Coefficient of variation (CV)0.8913475
Kurtosis-0.20065837
Mean9.4501474
Median Absolute Deviation (MAD)6.06
Skewness-0.01927229
Sum3974268.9
Variance70.953081
MonotonicityNot monotonic
2023-09-12T20:13:22.776802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.13 245
 
0.1%
5.2 238
 
0.1%
10.33 238
 
0.1%
8.59 238
 
0.1%
11.93 237
 
0.1%
12 235
 
0.1%
6.97 234
 
0.1%
10.4 234
 
0.1%
8.1 232
 
0.1%
5.53 231
 
0.1%
Other values (5520) 418189
99.4%
ValueCountFrequency (%)
-23.01 1
< 0.1%
-22.91 1
< 0.1%
-22.76 1
< 0.1%
-22.64 1
< 0.1%
-22.63 1
< 0.1%
-22.55 2
< 0.1%
-22.54 1
< 0.1%
-22.5 1
< 0.1%
-22.49 2
< 0.1%
-22.47 1
< 0.1%
ValueCountFrequency (%)
37.28 1
 
< 0.1%
37.13 1
 
< 0.1%
37.1 1
 
< 0.1%
37.09 1
 
< 0.1%
37.01 1
 
< 0.1%
36.87 3
< 0.1%
36.86 1
 
< 0.1%
36.79 1
 
< 0.1%
36.77 1
 
< 0.1%
36.71 1
 
< 0.1%

Tpot (K)
Real number (ℝ)

Distinct5639
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.49274
Minimum250.6
Maximum311.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:22.829345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum250.6
5-th percentile270.04
Q1277.43
median283.47
Q3289.53
95-th percentile297.26
Maximum311.34
Range60.74
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation8.5044714
Coefficient of variation (CV)0.029998903
Kurtosis-0.13967191
Mean283.49274
Median Absolute Deviation (MAD)6.05
Skewness-0.042512193
Sum1.1922316 × 108
Variance72.326034
MonotonicityNot monotonic
2023-09-12T20:13:22.878999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
282.69 223
 
0.1%
282.68 222
 
0.1%
281.46 220
 
0.1%
281.57 216
 
0.1%
282.2 215
 
0.1%
281.89 213
 
0.1%
282.66 213
 
0.1%
281.36 213
 
0.1%
288.6 211
 
0.1%
282.33 209
 
< 0.1%
Other values (5629) 418396
99.5%
ValueCountFrequency (%)
250.6 1
< 0.1%
250.71 1
< 0.1%
250.85 1
< 0.1%
250.98 1
< 0.1%
251.06 1
< 0.1%
251.09 1
< 0.1%
251.17 2
< 0.1%
251.18 1
< 0.1%
251.22 2
< 0.1%
251.23 1
< 0.1%
ValueCountFrequency (%)
311.34 1
< 0.1%
311.21 1
< 0.1%
311.19 1
< 0.1%
311.06 1
< 0.1%
311.04 1
< 0.1%
311.02 1
< 0.1%
310.98 1
< 0.1%
310.97 1
< 0.1%
310.81 1
< 0.1%
310.78 1
< 0.1%

Tdew (degC)
Real number (ℝ)

Distinct4343
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9558538
Minimum-25.01
Maximum23.11
Zeros216
Zeros (%)0.1%
Negative99829
Negative (%)23.7%
Memory size3.2 MiB
2023-09-12T20:13:22.931228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-25.01
5-th percentile-6.45
Q10.24
median5.22
Q310.07
95-th percentile15.14
Maximum23.11
Range48.12
Interquartile range (IQR)9.83

Descriptive statistics

Standard deviation6.7306743
Coefficient of variation (CV)1.3581261
Kurtosis-0.01879837
Mean4.9558538
Median Absolute Deviation (MAD)4.92
Skewness-0.37711004
Sum2084189.3
Variance45.301977
MonotonicityNot monotonic
2023-09-12T20:13:22.981633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.32 280
 
0.1%
8.13 278
 
0.1%
8.42 277
 
0.1%
8.47 275
 
0.1%
8.09 273
 
0.1%
8.46 272
 
0.1%
9.38 271
 
0.1%
8.27 267
 
0.1%
8.3 266
 
0.1%
9.22 265
 
0.1%
Other values (4333) 417827
99.4%
ValueCountFrequency (%)
-25.01 1
< 0.1%
-24.85 1
< 0.1%
-24.8 1
< 0.1%
-24.71 1
< 0.1%
-24.66 1
< 0.1%
-24.63 1
< 0.1%
-24.61 1
< 0.1%
-24.58 1
< 0.1%
-24.55 1
< 0.1%
-24.52 1
< 0.1%
ValueCountFrequency (%)
23.11 1
< 0.1%
23.07 1
< 0.1%
23.06 2
< 0.1%
22.94 2
< 0.1%
22.86 1
< 0.1%
22.83 1
< 0.1%
22.64 1
< 0.1%
22.4 1
< 0.1%
22.21 2
< 0.1%
22.2 2
< 0.1%

rh (%)
Real number (ℝ)

Distinct4805
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.008259
Minimum12.95
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.033081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.95
5-th percentile44.105
Q165.21
median79.3
Q389.4
95-th percentile97.2
Maximum100
Range87.05
Interquartile range (IQR)24.19

Descriptive statistics

Standard deviation16.476175
Coefficient of variation (CV)0.21676822
Kurtosis-0.37445008
Mean76.008259
Median Absolute Deviation (MAD)11.5
Skewness-0.67215791
Sum31965350
Variance271.46435
MonotonicityNot monotonic
2023-09-12T20:13:23.082223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1748
 
0.4%
90.5 1227
 
0.3%
94.8 1179
 
0.3%
93.9 1174
 
0.3%
90.6 1174
 
0.3%
90.2 1174
 
0.3%
90.1 1170
 
0.3%
94.2 1170
 
0.3%
88 1167
 
0.3%
90.9 1161
 
0.3%
Other values (4795) 408207
97.1%
ValueCountFrequency (%)
12.95 1
< 0.1%
13.06 1
< 0.1%
13.52 1
< 0.1%
13.56 1
< 0.1%
13.88 2
< 0.1%
14.13 1
< 0.1%
14.2 1
< 0.1%
14.3 1
< 0.1%
14.44 1
< 0.1%
15.87 1
< 0.1%
ValueCountFrequency (%)
100 1748
0.4%
99.9 396
 
0.1%
99.8 371
 
0.1%
99.7 442
 
0.1%
99.6 529
 
0.1%
99.5 550
 
0.1%
99.4 611
 
0.1%
99.3 621
 
0.1%
99.2 722
0.2%
99.1 768
0.2%

VPmax (mbar)
Real number (ℝ)

Distinct3658
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.576251
Minimum0.95
Maximum63.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.133161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile4.59
Q17.78
median11.82
Q317.6
95-th percentile28.4
Maximum63.77
Range62.82
Interquartile range (IQR)9.82

Descriptive statistics

Standard deviation7.7390201
Coefficient of variation (CV)0.57004105
Kurtosis2.3970634
Mean13.576251
Median Absolute Deviation (MAD)4.64
Skewness1.3110424
Sum5709505.7
Variance59.892431
MonotonicityNot monotonic
2023-09-12T20:13:23.183798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.83 420
 
0.1%
10.26 408
 
0.1%
10.64 406
 
0.1%
12.02 402
 
0.1%
10.86 397
 
0.1%
10.59 394
 
0.1%
10.22 390
 
0.1%
10.43 390
 
0.1%
11.94 389
 
0.1%
10.12 388
 
0.1%
Other values (3648) 416567
99.1%
ValueCountFrequency (%)
0.95 1
 
< 0.1%
0.96 1
 
< 0.1%
0.97 1
 
< 0.1%
0.98 2
 
< 0.1%
0.99 6
< 0.1%
1 6
< 0.1%
1.01 4
< 0.1%
1.02 4
< 0.1%
1.03 5
< 0.1%
1.05 3
< 0.1%
ValueCountFrequency (%)
63.77 1
 
< 0.1%
63.26 1
 
< 0.1%
63.15 1
 
< 0.1%
63.12 1
 
< 0.1%
62.85 1
 
< 0.1%
62.37 3
< 0.1%
62.33 1
 
< 0.1%
62.1 1
 
< 0.1%
62.03 1
 
< 0.1%
61.83 1
 
< 0.1%

VPact (mbar)
Real number (ℝ)

Distinct2438
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5337559
Minimum0.79
Maximum28.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.236758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile3.76
Q16.21
median8.86
Q312.35
95-th percentile17.23
Maximum28.32
Range27.53
Interquartile range (IQR)6.14

Descriptive statistics

Standard deviation4.1841643
Coefficient of variation (CV)0.4388789
Kurtosis-0.24977824
Mean9.5337559
Median Absolute Deviation (MAD)2.94
Skewness0.55660687
Sum4009430.6
Variance17.507231
MonotonicityNot monotonic
2023-09-12T20:13:23.284861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.92 560
 
0.1%
6.04 540
 
0.1%
6.03 538
 
0.1%
5.97 536
 
0.1%
6.08 536
 
0.1%
6 534
 
0.1%
6.09 534
 
0.1%
5.9 532
 
0.1%
5.84 531
 
0.1%
6.06 530
 
0.1%
Other values (2428) 415180
98.7%
ValueCountFrequency (%)
0.79 1
 
< 0.1%
0.8 1
 
< 0.1%
0.81 2
 
< 0.1%
0.82 4
< 0.1%
0.83 6
< 0.1%
0.84 9
< 0.1%
0.85 3
 
< 0.1%
0.86 3
 
< 0.1%
0.87 3
 
< 0.1%
0.88 1
 
< 0.1%
ValueCountFrequency (%)
28.32 1
< 0.1%
28.26 1
< 0.1%
28.25 1
< 0.1%
28.24 1
< 0.1%
28.04 2
< 0.1%
27.9 1
< 0.1%
27.86 1
< 0.1%
27.53 1
< 0.1%
27.14 1
< 0.1%
26.83 1
< 0.1%

VPdef (mbar)
Real number (ℝ)

Distinct3649
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0424116
Minimum0
Maximum46.01
Zeros1749
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.335915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21
Q10.87
median2.19
Q35.3
95-th percentile14.32
Maximum46.01
Range46.01
Interquartile range (IQR)4.43

Descriptive statistics

Standard deviation4.8968509
Coefficient of variation (CV)1.2113687
Kurtosis7.3591571
Mean4.0424116
Median Absolute Deviation (MAD)1.63
Skewness2.3654001
Sum1700040.2
Variance23.979149
MonotonicityNot monotonic
2023-09-12T20:13:23.386000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1749
 
0.4%
0.27 1477
 
0.4%
0.31 1465
 
0.3%
0.3 1460
 
0.3%
0.34 1444
 
0.3%
0.29 1435
 
0.3%
0.32 1424
 
0.3%
0.24 1407
 
0.3%
0.28 1406
 
0.3%
0.22 1396
 
0.3%
Other values (3639) 405888
96.5%
ValueCountFrequency (%)
0 1749
0.4%
0.01 588
 
0.1%
0.02 536
 
0.1%
0.03 642
 
0.2%
0.04 742
0.2%
0.05 772
0.2%
0.06 950
0.2%
0.07 1047
0.2%
0.08 1078
0.3%
0.09 974
0.2%
ValueCountFrequency (%)
46.01 1
< 0.1%
45.53 1
< 0.1%
45.42 1
< 0.1%
45.41 2
< 0.1%
45.2 1
< 0.1%
44.87 1
< 0.1%
44.83 1
< 0.1%
44.8 1
< 0.1%
44.76 1
< 0.1%
44.52 1
< 0.1%

sh (g/kg)
Real number (ℝ)

Distinct1600
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0224083
Minimum0.5
Maximum18.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.437160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.37
Q13.92
median5.59
Q37.8
95-th percentile10.92
Maximum18.13
Range17.63
Interquartile range (IQR)3.88

Descriptive statistics

Standard deviation2.656139
Coefficient of variation (CV)0.44104267
Kurtosis-0.22367576
Mean6.0224083
Median Absolute Deviation (MAD)1.86
Skewness0.56790519
Sum2532729.8
Variance7.0550745
MonotonicityNot monotonic
2023-09-12T20:13:23.487842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.79 870
 
0.2%
3.8 861
 
0.2%
3.78 849
 
0.2%
3.77 848
 
0.2%
3.81 839
 
0.2%
3.85 828
 
0.2%
3.74 826
 
0.2%
3.92 808
 
0.2%
3.69 806
 
0.2%
3.82 802
 
0.2%
Other values (1590) 412214
98.0%
ValueCountFrequency (%)
0.5 2
 
< 0.1%
0.51 4
 
< 0.1%
0.52 10
< 0.1%
0.53 10
< 0.1%
0.54 4
 
< 0.1%
0.55 3
 
< 0.1%
0.56 3
 
< 0.1%
0.57 9
< 0.1%
0.58 20
< 0.1%
0.59 11
< 0.1%
ValueCountFrequency (%)
18.13 1
< 0.1%
18.09 1
< 0.1%
18.07 2
< 0.1%
17.94 1
< 0.1%
17.93 1
< 0.1%
17.85 1
< 0.1%
17.82 1
< 0.1%
17.61 1
< 0.1%
17.36 1
< 0.1%
17.14 2
< 0.1%

H2OC (mmol/mol)
Real number (ℝ)

Distinct2483
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6402231
Minimum0.8
Maximum28.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.543286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3.8
Q16.29
median8.96
Q312.49
95-th percentile17.44
Maximum28.82
Range28.02
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation4.2353948
Coefficient of variation (CV)0.43934614
Kurtosis-0.23590689
Mean9.6402231
Median Absolute Deviation (MAD)2.97
Skewness0.56088997
Sum4054205.5
Variance17.938569
MonotonicityNot monotonic
2023-09-12T20:13:23.668247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.08 568
 
0.1%
6.09 558
 
0.1%
6.11 549
 
0.1%
6.06 547
 
0.1%
6.04 541
 
0.1%
6.13 535
 
0.1%
6.17 534
 
0.1%
5.91 519
 
0.1%
6.29 516
 
0.1%
6.18 511
 
0.1%
Other values (2473) 415173
98.7%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
0.81 2
 
< 0.1%
0.82 2
 
< 0.1%
0.83 5
< 0.1%
0.84 7
< 0.1%
0.85 7
< 0.1%
0.86 5
< 0.1%
0.87 1
 
< 0.1%
0.88 2
 
< 0.1%
0.89 1
 
< 0.1%
ValueCountFrequency (%)
28.82 1
< 0.1%
28.76 1
< 0.1%
28.74 1
< 0.1%
28.73 1
< 0.1%
28.53 1
< 0.1%
28.52 1
< 0.1%
28.39 1
< 0.1%
28.34 1
< 0.1%
28 1
< 0.1%
27.62 1
< 0.1%

rho (g/m**3)
Real number (ℝ)

Distinct22972
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1216.0627
Minimum1059.45
Maximum1393.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:23.723395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1059.45
5-th percentile1154.78
Q11187.49
median1213.79
Q31242.77
95-th percentile1282.33
Maximum1393.54
Range334.09
Interquartile range (IQR)55.28

Descriptive statistics

Standard deviation39.975208
Coefficient of variation (CV)0.032872653
Kurtosis0.13744658
Mean1216.0627
Median Absolute Deviation (MAD)27.53
Skewness0.3127057
Sum5.114164 × 108
Variance1598.0173
MonotonicityNot monotonic
2023-09-12T20:13:23.776423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1222.89 69
 
< 0.1%
1184.91 64
 
< 0.1%
1199.15 63
 
< 0.1%
1205.2 63
 
< 0.1%
1204.85 61
 
< 0.1%
1203.81 61
 
< 0.1%
1197.79 60
 
< 0.1%
1193.09 60
 
< 0.1%
1223.56 59
 
< 0.1%
1195.77 59
 
< 0.1%
Other values (22962) 419932
99.9%
ValueCountFrequency (%)
1059.45 1
< 0.1%
1059.51 1
< 0.1%
1063.57 1
< 0.1%
1064.26 1
< 0.1%
1064.65 1
< 0.1%
1066.19 1
< 0.1%
1100.15 1
< 0.1%
1100.38 1
< 0.1%
1101.07 1
< 0.1%
1101.39 1
< 0.1%
ValueCountFrequency (%)
1393.54 1
< 0.1%
1393.26 1
< 0.1%
1392.56 1
< 0.1%
1392.29 1
< 0.1%
1392.1 1
< 0.1%
1391.88 1
< 0.1%
1391.82 1
< 0.1%
1391.63 1
< 0.1%
1391.62 1
< 0.1%
1391.6 2
< 0.1%

wv (m/s)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1193
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7022238
Minimum-9999
Maximum28.49
Zeros484
Zeros (%)0.1%
Negative18
Negative (%)< 0.1%
Memory size3.2 MiB
2023-09-12T20:13:23.834284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.41
Q10.99
median1.76
Q32.86
95-th percentile5.18
Maximum28.49
Range10027.49
Interquartile range (IQR)1.87

Descriptive statistics

Standard deviation65.446714
Coefficient of variation (CV)38.447772
Kurtosis23333.282
Mean1.7022238
Median Absolute Deviation (MAD)0.88
Skewness-152.71609
Sum715871.94
Variance4283.2723
MonotonicityNot monotonic
2023-09-12T20:13:23.887822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98 1631
 
0.4%
0.86 1619
 
0.4%
0.92 1616
 
0.4%
1.08 1605
 
0.4%
0.91 1588
 
0.4%
0.95 1575
 
0.4%
0.79 1564
 
0.4%
0.84 1563
 
0.4%
1.06 1560
 
0.4%
0.72 1559
 
0.4%
Other values (1183) 404671
96.2%
ValueCountFrequency (%)
-9999 18
 
< 0.1%
0 484
0.1%
0.01 69
 
< 0.1%
0.02 58
 
< 0.1%
0.03 71
 
< 0.1%
0.04 74
 
< 0.1%
0.05 63
 
< 0.1%
0.06 56
 
< 0.1%
0.07 68
 
< 0.1%
0.08 55
 
< 0.1%
ValueCountFrequency (%)
28.49 1
< 0.1%
14.63 1
< 0.1%
14.09 1
< 0.1%
14.01 1
< 0.1%
13.95 1
< 0.1%
13.64 1
< 0.1%
13.59 1
< 0.1%
13.5 1
< 0.1%
13.19 1
< 0.1%
13.09 1
< 0.1%

max. wv (m/s)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1503
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0565553
Minimum-9999
Maximum23.5
Zeros435
Zeros (%)0.1%
Negative20
Negative (%)< 0.1%
Memory size3.2 MiB
2023-09-12T20:13:23.942259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.84
Q11.76
median2.96
Q34.74
95-th percentile8.09
Maximum23.5
Range10022.5
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation69.016932
Coefficient of variation (CV)22.579972
Kurtosis20974.474
Mean3.0565553
Median Absolute Deviation (MAD)1.38
Skewness-144.74887
Sum1285437.4
Variance4763.3369
MonotonicityNot monotonic
2023-09-12T20:13:23.996252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.88 4273
 
1.0%
2 3975
 
0.9%
1.64 3371
 
0.8%
1.76 3347
 
0.8%
2.88 3308
 
0.8%
1.52 3287
 
0.8%
0.88 3285
 
0.8%
1.68 3243
 
0.8%
1.36 3240
 
0.8%
1.92 3240
 
0.8%
Other values (1493) 385982
91.8%
ValueCountFrequency (%)
-9999 20
 
< 0.1%
0 435
0.1%
0.13 83
 
< 0.1%
0.2 4
 
< 0.1%
0.22 1
 
< 0.1%
0.24 21
 
< 0.1%
0.25 127
 
< 0.1%
0.26 10
 
< 0.1%
0.28 50
 
< 0.1%
0.3 17
 
< 0.1%
ValueCountFrequency (%)
23.5 1
< 0.1%
22.86 1
< 0.1%
22.26 1
< 0.1%
21.58 1
< 0.1%
20.99 1
< 0.1%
20.78 1
< 0.1%
20.69 1
< 0.1%
20.58 1
< 0.1%
20.49 1
< 0.1%
20.4 1
< 0.1%

wd (deg)
Real number (ℝ)

Distinct9893
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.74374
Minimum0
Maximum360
Zeros436
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-12T20:13:24.050364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.39
Q1124.9
median198.1
Q3234.1
95-th percentile289.9
Maximum360
Range360
Interquartile range (IQR)109.2

Descriptive statistics

Standard deviation86.681693
Coefficient of variation (CV)0.49605035
Kurtosis-0.62746528
Mean174.74374
Median Absolute Deviation (MAD)46.5
Skewness-0.49202149
Sum73488654
Variance7513.7159
MonotonicityNot monotonic
2023-09-12T20:13:24.103247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211.2 471
 
0.1%
209.3 457
 
0.1%
209.7 450
 
0.1%
208.7 450
 
0.1%
212 448
 
0.1%
210.4 446
 
0.1%
211.8 444
 
0.1%
210.9 443
 
0.1%
211.5 443
 
0.1%
211.1 441
 
0.1%
Other values (9883) 416058
98.9%
ValueCountFrequency (%)
0 436
0.1%
0.01 3
 
< 0.1%
0.02 8
 
< 0.1%
0.03 7
 
< 0.1%
0.04 6
 
< 0.1%
0.05 9
 
< 0.1%
0.06 4
 
< 0.1%
0.07 5
 
< 0.1%
0.08 6
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
360 24
 
< 0.1%
359.9 46
< 0.1%
359.8 71
< 0.1%
359.7 49
< 0.1%
359.6 58
< 0.1%
359.5 44
< 0.1%
359.4 59
< 0.1%
359.3 46
< 0.1%
359.2 56
< 0.1%
359.1 52
< 0.1%

Interactions

2023-09-12T20:13:20.274071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:04.701998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.045908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.210368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.440065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.566739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.795588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.940940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.151611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.294553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.540900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.690287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.928685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.059719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.362547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:04.856609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.126255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.290949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.520195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.649915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.877031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.022952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.234392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.378072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.624544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.772995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.009134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.139947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.451670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.044630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.213142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.373032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.599842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.732817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.958465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.104401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.315604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.461421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.708199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.856614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.089024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.220706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.542123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.127386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.300532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.530362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.679308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.816741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.041793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.185983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.397706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.544953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.790404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.939917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.170363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.301058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.628944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.207752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.381895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.610474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.758307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.968746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.122541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.264955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.477501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.626120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.872642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.021580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.248881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.379898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.717269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.296344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.465291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.689690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.835981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.048742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.200594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.416111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.557705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.707408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.952641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.102886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.327585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.458638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.806726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.381901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.548021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.773175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.916136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.131176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.283700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.496181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.639980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.865456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.034537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.260255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.409293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.539207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.895209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.464758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.628772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.854918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.996077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.213253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.363835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.576146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.718557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.949027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.115177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.344150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.492065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.693628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.981918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.547505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.711437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.938052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.074278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.293788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.443733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.654997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.796432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.030226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.196519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.426482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.572470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.771638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:21.073674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.633876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.795416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.023628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.157100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.379044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.527114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.738579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.886601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.115770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.280037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.512514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.656169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.855616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:21.162597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.715029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.877895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.105874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.238194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.462318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.609500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.821123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.967959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.200450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.361223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.594782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.736576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:19.937747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:21.253984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.799938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:06.962140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.191803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.320868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.547660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.693863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.903929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.051155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.287721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.445396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.679686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.817965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.021144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:21.342586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.879669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.043777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.273083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.400581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.629398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.774270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:12.984544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.129846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.371161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.525280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.761619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.897249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.101356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:21.429888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:05.962234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:07.127535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:08.357510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:09.483587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:10.713478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:11.858271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:13.068525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:14.212571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:15.455839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:16.608353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:17.846222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:18.978203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T20:13:20.184248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-12T20:13:24.156083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
p (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wd (deg)
p (mbar)1.000-0.055-0.129-0.071-0.002-0.055-0.071-0.028-0.087-0.0870.296-0.193-0.185-0.052
T (degC)-0.0551.0000.9970.899-0.5381.0000.8990.7990.8980.898-0.9630.0880.1220.053
Tpot (K)-0.1290.9971.0000.897-0.5350.9970.8970.7950.8980.898-0.9820.1030.1360.056
Tdew (degC)-0.0710.8990.8971.000-0.1620.8991.0000.5001.0001.000-0.878-0.063-0.0460.041
rh (%)-0.002-0.538-0.535-0.1621.000-0.538-0.162-0.925-0.161-0.1610.499-0.346-0.391-0.065
VPmax (mbar)-0.0551.0000.9970.899-0.5381.0000.8990.7990.8980.898-0.9630.0880.1220.053
VPact (mbar)-0.0710.8990.8971.000-0.1620.8991.0000.5001.0001.000-0.878-0.063-0.0460.041
VPdef (mbar)-0.0280.7990.7950.500-0.9250.7990.5001.0000.5000.500-0.7560.2770.3230.080
sh (g/kg)-0.0870.8980.8981.000-0.1610.8981.0000.5001.0001.000-0.882-0.060-0.0430.041
H2OC (mmol/mol)-0.0870.8980.8981.000-0.1610.8981.0000.5001.0001.000-0.882-0.060-0.0430.041
rho (g/m**3)0.296-0.963-0.982-0.8780.499-0.963-0.878-0.756-0.882-0.8821.000-0.127-0.157-0.059
wv (m/s)-0.1930.0880.103-0.063-0.3460.088-0.0630.277-0.060-0.060-0.1271.0000.958-0.015
max. wv (m/s)-0.1850.1220.136-0.046-0.3910.122-0.0460.323-0.043-0.043-0.1570.9581.0000.034
wd (deg)-0.0520.0530.0560.041-0.0650.0530.0410.0800.0410.041-0.059-0.0150.0341.000

Missing values

2023-09-12T20:13:21.540562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-12T20:13:21.820902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Date Timep (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wd (deg)
001.01.2009 00:10:00996.52-8.02265.40-8.9093.33.333.110.221.943.121307.751.031.75152.3
101.01.2009 00:20:00996.57-8.41265.01-9.2893.43.233.020.211.893.031309.800.721.50136.1
201.01.2009 00:30:00996.53-8.51264.91-9.3193.93.213.010.201.883.021310.240.190.63171.6
301.01.2009 00:40:00996.51-8.31265.12-9.0794.23.263.070.191.923.081309.190.340.50198.0
401.01.2009 00:50:00996.51-8.27265.15-9.0494.13.273.080.191.923.091309.000.320.63214.3
501.01.2009 01:00:00996.50-8.05265.38-8.7894.43.333.140.191.963.151307.860.210.63192.7
601.01.2009 01:10:00996.50-7.62265.81-8.3094.83.443.260.182.043.271305.680.180.63166.5
701.01.2009 01:20:00996.50-7.62265.81-8.3694.43.443.250.192.033.261305.690.190.50118.6
801.01.2009 01:30:00996.50-7.91265.52-8.7393.83.363.150.211.973.161307.170.280.75188.5
901.01.2009 01:40:00996.53-8.43264.99-9.3493.13.233.000.221.883.021309.850.590.88185.0
Date Timep (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wd (deg)
42054131.12.2016 22:30:001000.44-4.08269.05-7.8974.604.513.371.152.103.371293.551.272.48192.1
42054231.12.2016 22:40:001000.45-4.45268.68-7.1581.304.393.570.822.223.571295.240.801.44183.8
42054331.12.2016 22:50:001000.32-4.09269.05-7.2378.604.513.540.962.213.541293.371.251.60199.2
42054431.12.2016 23:00:001000.21-3.76269.39-7.9572.504.623.351.272.093.351291.710.891.30223.7
42054531.12.2016 23:10:001000.11-3.93269.23-8.0972.604.563.311.252.063.311292.410.561.00202.6
42054631.12.2016 23:20:001000.07-4.05269.10-8.1373.104.523.301.222.063.301292.980.671.52240.0
42054731.12.2016 23:30:00999.93-3.35269.81-8.0669.714.773.321.442.073.321289.441.141.92234.3
42054831.12.2016 23:40:00999.82-3.16270.01-8.2167.914.843.281.552.053.281288.391.082.00215.2
42054931.12.2016 23:50:00999.81-4.23268.94-8.5371.804.463.201.261.993.201293.561.492.16225.8
42055001.01.2017 00:00:00999.82-4.82268.36-8.4275.704.273.231.042.013.231296.381.231.96184.9

Duplicate rows

Most frequently occurring

Date Timep (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wd (deg)# duplicates
001.07.2010 00:10:00992.0617.87291.6914.0678.420.5016.074.4310.1416.201180.210.310.5651.112
101.07.2010 00:20:00992.0217.82291.6514.0378.520.4416.044.3910.1216.171180.380.230.4852.642
201.07.2010 00:30:00992.0417.92291.7514.0978.320.5716.104.4610.1616.231179.970.180.4022.102
301.07.2010 00:40:00991.9617.82291.6514.0278.420.4416.024.4110.1116.151180.320.190.40354.802
401.07.2010 00:50:00991.9017.54291.3813.9679.520.0815.964.1210.0716.101181.410.240.9821.402
501.07.2010 01:00:00991.8117.35291.1913.8780.019.8415.873.9710.0216.001182.120.440.9649.642
601.07.2010 01:10:00991.8117.11290.9513.8381.019.5415.833.719.9915.961183.110.340.76296.102
701.07.2010 01:20:00991.8516.90290.7413.7481.619.2815.743.559.9315.871184.061.211.76239.502
801.07.2010 01:30:00991.8216.87290.7113.6181.119.2515.613.649.8515.741184.211.752.28222.202
901.07.2010 01:40:00991.8116.69290.5313.5981.919.0315.583.449.8315.711184.941.041.64209.902